A Machine Learning-Based Two-Dimensional Wavelet Transform for Damage Detection in Π-Shape Laminated Composite Frames

This paper introduces a robust damage detection strategy for π-shape laminated composite frames. A machine learning-based two-dimensional wavelet transform scheme processes one-dimensional signals by converting them into a two-dimensional format, enabling analysis of the signal and its details using a two-dimensional discrete wavelet transform in both horizontal and vertical directions. The proposed method transfers the damage information hidden in the signal from the one-dimensional graph domain to the pixel domain to accurately locate the damage position in the structural mode shape signals. A neural network is used to select the optimized vanishing moments in terms of wavelet coefficient properties. Results show that the proposed machine learning-based two-dimensional approach outperforms the traditional one-dimensional wavelet transform in detecting damage. The quantitative evaluation demonstrates high performance for the proposed GMDH model, achieving regression indices (R) of 0.8926 and 0.9091, along with Mean Squared Errors (MSE) of 0.1191 and 0.1056 for the training and testing phases, respectively, so that GMDH algorithm effectively predicts vanishing moments of the wavelet function with minimal error.